Fix more bugs (#232)
* Update gemma.py * position_ids * Update gemma.py * Update gemma.py * pos * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update cross_entropy_loss.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * revert * revert * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update cross_entropy_loss.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * rope * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * llama * Update llama.py * gemma * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update save.py * RoPE * Update llama.py * Update llama.py * Update llama.py * Update gemma.py * correct_dtype * Update gemma.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Chat Templates * Update README.md * Update README.md * Update llama.py * DoRA * Update _utils.py * Update chat_templates.py * Update llama.py * Hotfix - fix DoRA, Gemma prompt template (#202) (#203) * Update save.py * saving * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update __init__.py * Update save.py * Update save.py * Update save.py * save * trainer * spaces * original * Gemma * Update pyproject.toml * Update mapper.py * Update fast_lora.py * FastGemmaModel * model_type * Update llama.py * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update llama.py * Update fast_lora.py * Update llama.py * Update llama.py * Update cross_entropy_loss.py * Update llama.py * Update llama.py * gemma * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update fast_lora.py * Update fast_lora.py * Fast CE Loss * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * CE * Update llama.py * Update llama.py * Update cross_entropy_loss.py * Update geglu.py * Update cross_entropy_loss.py * revert * Update llama.py * Update llama.py * norm * Update gemma.py * Update gemma.py * position_ids * Update gemma.py * Update gemma.py * pos * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update cross_entropy_loss.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * revert * revert * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update cross_entropy_loss.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * rope * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * llama * Update llama.py * gemma * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update save.py * RoPE * Update llama.py * Update llama.py * Update llama.py * Update gemma.py * correct_dtype * Update gemma.py * Update cross_entropy_loss.py * Update cross_entropy_loss.py * Chat Templates * Update README.md * Update README.md * Update llama.py * DoRA * Update _utils.py * Update chat_templates.py * Update pyproject.toml * Small fixes * Update pyproject.toml * Approx gelu * Update geglu.py * Approx gelu * Update llama.py * Update __init__.py * Update __init__.py * Update _utils.py * Update geglu.py * Update gemma.py * Update rms_layernorm.py * Update rms_layernorm.py * Update rms_layernorm.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Fix Gemma merging * Update rms_layernorm.py * Update gemma.py * Update pyproject.toml * Layernorms * Gemma precision * Update gemma.py * sqrt * Update gemma.py * Update save.py * RoPE and Gemma precision * Update rms_layernorm.py * Fix warning * Update chat_templates.py * Update chat_templates.py * Update save.py * Update save.py * Update save.py * Update chat_templates.py * Update llama.py * model_name * Update loader.py * Tokenizer overwritten * Update llama.py * Update llama.py * Update llama.py * Update save.py * Accuracy * Revert * Update save.py * Update fast_lora.py * Update fast_lora.py * Update fast_lora.py * Update fast_lora.py * Update fast_lora.py * Update chat_templates.py * Update save.py * Update save.py * Update llama.py * Update llama.py * Account for DoRA * Update llama.py
This commit is contained in:
Daniel Han
GitHub
parent
e375c5813e
commit
dd72d9f806
4 changed files with 114 additions and 25 deletions
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@ -253,13 +253,15 @@ def get_chat_template(
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mapping = {"role" : "role", "content" : "content", "user" : "user", "assistant" : "assistant"},
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mapping = {"role" : "role", "content" : "content", "user" : "user", "assistant" : "assistant"},
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map_eos_token = True,
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map_eos_token = True,
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):
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):
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old_tokenizer = tokenizer
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if map_eos_token is False:
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if map_eos_token is False:
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assert("Unsloth: Can only map new tokens to EOS for now. Adding new tokens is not yet supported.")
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assert("Unsloth: Can only map new tokens to EOS for now. Adding new tokens is not yet supported.")
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pass
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pass
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# if tokenizer.__class__.__name__.startswith("Gemma") and chat_template == "chatml":
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if tokenizer.__class__.__name__.startswith("Gemma") and chat_template == "chatml":
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# chat_template = "gemma_chatml"
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chat_template = "gemma_chatml"
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# pass
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pass
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old_padding_side = tokenizer.padding_side
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old_padding_side = tokenizer.padding_side
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@ -340,6 +342,17 @@ def get_chat_template(
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tokenizer.padding_side = old_padding_side
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tokenizer.padding_side = old_padding_side
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tokenizer.chat_template = chat_template
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tokenizer.chat_template = chat_template
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# Also fix up other tokens
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old_pad_token = getattr(old_tokenizer, "pad_token", None)
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old_bos_token = getattr(old_tokenizer, "bos_token", None)
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old_unk_token = getattr(old_tokenizer, "unk_token", None)
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new_pad_token = getattr(tokenizer, "pad_token", None)
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new_bos_token = getattr(tokenizer, "bos_token", None)
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new_unk_token = getattr(tokenizer, "unk_token", None)
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if old_pad_token != new_pad_token: tokenizer.pad_token = old_pad_token
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if old_bos_token != new_bos_token: tokenizer.bos_token = old_bos_token
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if old_unk_token != new_unk_token: tokenizer.unk_token = old_unk_token
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#stopping_criteria = create_stopping_criteria(tokenizer, stop_word)
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#stopping_criteria = create_stopping_criteria(tokenizer, stop_word)
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return tokenizer#, stopping_criteria
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return tokenizer#, stopping_criteria
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@ -95,7 +95,7 @@ def prepare_model_for_kbit_training(
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# Freeze all parameters except LoRA
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# Freeze all parameters except LoRA
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for name, param in model.named_parameters():
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for name, param in model.named_parameters():
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if ".lora_A." in name or ".lora_B." in name:
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if ".lora_A." in name or ".lora_B." in name or ".lora_magnitude_vector" in name:
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param.requires_grad_(True)
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param.requires_grad_(True)
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else:
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else:
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param.requires_grad_(False)
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param.requires_grad_(False)
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@ -511,26 +511,36 @@ def LlamaModel_fast_forward(
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# Mormalized from Gemma
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# Mormalized from Gemma
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IS_GEMMA = self.config.model_type == "gemma"
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IS_GEMMA = self.config.model_type == "gemma"
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train_embed_tokens = self.embed_tokens.weight.requires_grad
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if IS_GEMMA:
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if IS_GEMMA:
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inputs_requires_grad = inputs_embeds.requires_grad
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if not inputs_embeds.is_leaf:
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inputs_embeds = inputs_embeds.detach()
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inputs_requires_grad = True
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elif inputs_requires_grad:
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inputs_embeds.requires_grad_(False)
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pass
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# Match Gemma exactly by casting to bfloat16 / float16
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# Match Gemma exactly by casting to bfloat16 / float16
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# inputs_embeds *= math_sqrt(self.config.hidden_size)
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# inputs_embeds *= math_sqrt(self.config.hidden_size)
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# Ie 3072**0.5 = 55.5000 in bfloat16, whilst 55.4256 in float32
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# Ie 3072**0.5 = 55.5000 in bfloat16, whilst 55.4256 in float32
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# & 2048**0.5 = 45.2500 in bfloat16, whilst 45.2548 in float32
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# & 2048**0.5 = 45.2500 in bfloat16, whilst 45.2548 in float32
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inputs_embeds *= torch.tensor(math_sqrt(self.config.hidden_size), dtype = inputs_embeds.dtype)
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normalizer = torch.tensor(math_sqrt(self.config.hidden_size), dtype = inputs_embeds.dtype)
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# inputs_embeds *= math_sqrt(self.config.hidden_size)
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if inputs_requires_grad: inputs_embeds.requires_grad_(True)
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if train_embed_tokens:
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# Careful we must not do an inplace op!
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inputs_embeds = inputs_embeds * normalizer
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else:
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inputs_requires_grad = inputs_embeds.requires_grad
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if not inputs_embeds.is_leaf:
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inputs_embeds = inputs_embeds.detach()
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inputs_requires_grad = True
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elif inputs_requires_grad:
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inputs_embeds.requires_grad_(False)
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pass
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inputs_embeds *= normalizer
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# inputs_embeds *= math_sqrt(self.config.hidden_size)
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if inputs_requires_grad: inputs_embeds.requires_grad_(True)
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pass
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pass
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pass
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# Fix up attention mask by setting elements to 0
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# Fix up attention mask by setting elements to 0
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# Specifically for DPO
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# Specifically for DPO
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if self._has_no_labels and (attention_mask is not None) and (past_key_values is None):
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if self._has_no_labels and (attention_mask is not None) and (past_key_values is None) and \
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(not train_embed_tokens):
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# Careful for inference the attention_mask is size (1, kv_seq_len)
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# Careful for inference the attention_mask is size (1, kv_seq_len)
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# Whilst the input_embeds is size (1, 1, 4096)
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# Whilst the input_embeds is size (1, 1, 4096)
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inputs_requires_grad = inputs_embeds.requires_grad
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inputs_requires_grad = inputs_embeds.requires_grad
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@ -1226,6 +1236,7 @@ class FastLlamaModel:
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random_state = 3407,
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random_state = 3407,
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max_seq_length = 2048, # not used anymore
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max_seq_length = 2048, # not used anymore
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use_rslora = False,
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use_rslora = False,
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modules_to_save = None,
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init_lora_weights = True,
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init_lora_weights = True,
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loftq_config = {},
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loftq_config = {},
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**kwargs,
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**kwargs,
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@ -1312,15 +1323,45 @@ class FastLlamaModel:
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accepted_modules = frozenset(("q_proj", "k_proj", "v_proj", "o_proj",
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accepted_modules = frozenset(("q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj",),)
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"gate_proj", "up_proj", "down_proj",),)
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model.config.update({"unsloth_version" : __version__})
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model.config.update({"unsloth_version" : __version__})
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train_lm_head = False
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train_embed_tokens = False
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final_modules = []
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for module in target_modules:
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for module in target_modules:
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assert(module in accepted_modules)
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if module == "lm_head":
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logger.warning_once(
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"Unsloth: `lm_head` should be placed in `modules_to_save` and not `target_modules`."\
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"We shall do it for you!"
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)
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train_lm_head = True
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elif module == "embed_tokens":
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logger.warning_once(
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"Unsloth: `embed_tokens` should be placed in `modules_to_save` and not `target_modules`."\
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"We shall do it for you!"
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)
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train_embed_tokens = True
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else:
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assert(module in accepted_modules)
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final_modules.append(module)
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pass
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# Check modules_to_save
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if modules_to_save is not None:
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for module in modules_to_save:
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if module == "lm_head":
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train_lm_head = True
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elif module == "embed_tokens":
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train_embed_tokens = True
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pass
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pass
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pass
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# Get LoRA
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# Get LoRA
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arguments = dict(
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arguments = dict(
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r = r,
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r = r,
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lora_alpha = lora_alpha,
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lora_alpha = lora_alpha,
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target_modules = target_modules,
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target_modules = final_modules,
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lora_dropout = lora_dropout,
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lora_dropout = lora_dropout,
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bias = bias,
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bias = bias,
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task_type = TaskType.CAUSAL_LM,
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task_type = TaskType.CAUSAL_LM,
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@ -1328,6 +1369,7 @@ class FastLlamaModel:
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init_lora_weights = init_lora_weights,
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init_lora_weights = init_lora_weights,
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loftq_config = loftq_config,
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loftq_config = loftq_config,
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use_rslora = use_rslora,
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use_rslora = use_rslora,
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modules_to_save = modules_to_save,
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**kwargs,
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**kwargs,
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)
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)
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if not SUPPORTS_LOFTQ: del arguments["loftq_config"]
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if not SUPPORTS_LOFTQ: del arguments["loftq_config"]
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@ -1337,6 +1379,14 @@ class FastLlamaModel:
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model = _get_peft_model(model, lora_config)
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model = _get_peft_model(model, lora_config)
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model = FastLlamaModel.patch_peft_model(model, use_gradient_checkpointing)
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model = FastLlamaModel.patch_peft_model(model, use_gradient_checkpointing)
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# Now patch lm_head and embed_tokens
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if train_embed_tokens:
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model.model.model.embed_tokens.requires_grad_(True)
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if train_lm_head:
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model.model.lm_head.requires_grad_(True)
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pass
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return model
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return model
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pass
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pass
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@ -1427,9 +1477,12 @@ class FastLlamaModel:
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if hasattr(gate_proj, "lora_A") and \
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if hasattr(gate_proj, "lora_A") and \
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hasattr( up_proj, "lora_A") and \
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hasattr( up_proj, "lora_A") and \
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hasattr(down_proj, "lora_A") and \
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hasattr(down_proj, "lora_A") and \
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(gate_proj.base_layer if hasattr(gate_proj, "base_layer") else gate_proj).bias is None and \
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((gate_proj.base_layer if hasattr(gate_proj, "base_layer") else gate_proj).bias is None) and \
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( up_proj.base_layer if hasattr( up_proj, "base_layer") else up_proj).bias is None and \
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(( up_proj.base_layer if hasattr( up_proj, "base_layer") else up_proj).bias is None) and \
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(down_proj.base_layer if hasattr(down_proj, "base_layer") else down_proj).bias is None:
|
((down_proj.base_layer if hasattr(down_proj, "base_layer") else down_proj).bias is None) and \
|
||||||
|
((gate_proj.lora_magnitude_vector if hasattr(gate_proj, "lora_magnitude_vector") else None) is None) and \
|
||||||
|
(( up_proj.lora_magnitude_vector if hasattr( up_proj, "lora_magnitude_vector") else None) is None) and \
|
||||||
|
((down_proj.lora_magnitude_vector if hasattr(down_proj, "lora_magnitude_vector") else None) is None):
|
||||||
|
|
||||||
# https://stackoverflow.com/questions/50599045/python-replacing-a-function-within-a-class-of-a-module
|
# https://stackoverflow.com/questions/50599045/python-replacing-a-function-within-a-class-of-a-module
|
||||||
layer.mlp.forward = types.MethodType(apply_lora_mlp, layer.mlp)
|
layer.mlp.forward = types.MethodType(apply_lora_mlp, layer.mlp)
|
||||||
|
|
@ -1448,9 +1501,12 @@ class FastLlamaModel:
|
||||||
if hasattr(q_proj, "lora_A") and \
|
if hasattr(q_proj, "lora_A") and \
|
||||||
hasattr(k_proj, "lora_A") and \
|
hasattr(k_proj, "lora_A") and \
|
||||||
hasattr(v_proj, "lora_A") and \
|
hasattr(v_proj, "lora_A") and \
|
||||||
(q_proj.base_layer if hasattr(q_proj, "base_layer") else q_proj).bias is None and \
|
((q_proj.base_layer if hasattr(q_proj, "base_layer") else q_proj).bias is None) and \
|
||||||
(k_proj.base_layer if hasattr(k_proj, "base_layer") else k_proj).bias is None and \
|
((k_proj.base_layer if hasattr(k_proj, "base_layer") else k_proj).bias is None) and \
|
||||||
(v_proj.base_layer if hasattr(v_proj, "base_layer") else v_proj).bias is None:
|
((v_proj.base_layer if hasattr(v_proj, "base_layer") else v_proj).bias is None) and \
|
||||||
|
((q_proj.lora_magnitude_vector if hasattr(q_proj, "lora_magnitude_vector") else None) is None) and \
|
||||||
|
((k_proj.lora_magnitude_vector if hasattr(k_proj, "lora_magnitude_vector") else None) is None) and \
|
||||||
|
((v_proj.lora_magnitude_vector if hasattr(v_proj, "lora_magnitude_vector") else None) is None):
|
||||||
|
|
||||||
layer.self_attn.apply_qkv = apply_lora_qkv
|
layer.self_attn.apply_qkv = apply_lora_qkv
|
||||||
n_qkv += 1
|
n_qkv += 1
|
||||||
|
|
@ -1464,7 +1520,8 @@ class FastLlamaModel:
|
||||||
# O attention patching
|
# O attention patching
|
||||||
o_proj = layer.self_attn.o_proj
|
o_proj = layer.self_attn.o_proj
|
||||||
if hasattr(o_proj, "lora_A") and \
|
if hasattr(o_proj, "lora_A") and \
|
||||||
(o_proj.base_layer if hasattr(o_proj, "base_layer") else o_proj).bias is None:
|
((o_proj.base_layer if hasattr(o_proj, "base_layer") else o_proj).bias is None) and \
|
||||||
|
((o_proj.lora_magnitude_vector if hasattr(o_proj, "lora_magnitude_vector") else None) is None):
|
||||||
|
|
||||||
layer.self_attn.apply_o = apply_lora_o
|
layer.self_attn.apply_o = apply_lora_o
|
||||||
n_o += 1
|
n_o += 1
|
||||||
|
|
|
||||||
|
|
@ -203,7 +203,11 @@ def unsloth_save_model(
|
||||||
|
|
||||||
print("Unsloth: Merging 4bit and LoRA weights to 4bit...")
|
print("Unsloth: Merging 4bit and LoRA weights to 4bit...")
|
||||||
print("This might take 5 minutes...")
|
print("This might take 5 minutes...")
|
||||||
model = model.merge_and_unload()
|
|
||||||
|
# Counteract no LoRA adapters!
|
||||||
|
if hasattr(model, "merge_and_unload"):
|
||||||
|
model = model.merge_and_unload()
|
||||||
|
pass
|
||||||
print("Done.")
|
print("Done.")
|
||||||
pass
|
pass
|
||||||
|
|
||||||
|
|
@ -573,6 +577,21 @@ def install_llama_cpp_old(version = -10):
|
||||||
latest = releases[-1]
|
latest = releases[-1]
|
||||||
version = releases[version].split(" ")[0]
|
version = releases[version].split(" ")[0]
|
||||||
|
|
||||||
|
# Check if the llama.cpp exists
|
||||||
|
if os.path.exists("llama.cpp"):
|
||||||
|
print(
|
||||||
|
"**[WARNING]** You have a llama.cpp old directory which is broken.\n"\
|
||||||
|
"Unsloth will DELETE the broken directory and install a new one.\n"\
|
||||||
|
"Press CTRL + C / cancel this if this is wrong. We shall wait 10 seconds.\n"
|
||||||
|
)
|
||||||
|
import time
|
||||||
|
for i in range(10):
|
||||||
|
print(f"**[WARNING]** Deleting llama.cpp directory... {10-i} seconds left.")
|
||||||
|
time.sleep(1)
|
||||||
|
import shutil
|
||||||
|
shutil.rmtree("llama.cpp")
|
||||||
|
pass
|
||||||
|
|
||||||
# Clone a specific commit
|
# Clone a specific commit
|
||||||
commands = [
|
commands = [
|
||||||
"git clone https://github.com/ggerganov/llama.cpp",
|
"git clone https://github.com/ggerganov/llama.cpp",
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue