staerkjo commited on
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Training in progress, step 13

Browse files
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
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+ library_name: transformers
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+ model_name: checkpoints
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+ tags:
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+ - generated_from_trainer
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+ - unsloth
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+ - sft
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+ - trl
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+ licence: license
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+ ---
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+
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+ # Model Card for checkpoints
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+
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+ This model is a fine-tuned version of [unsloth/meta-llama-3.1-8b-instruct-bnb-4bit](https://huggingface.co/unsloth/meta-llama-3.1-8b-instruct-bnb-4bit).
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+ It has been trained using [TRL](https://github.com/huggingface/trl).
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+
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+ ## Quick start
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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+ generator = pipeline("text-generation", model="staerkjo/checkpoints", device="cuda")
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+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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+ print(output["generated_text"])
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+ ```
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+
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+ ## Training procedure
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+
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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jojs-it-universitetet-i-k-benhavn/thesis-it-ticket-classification/runs/902o77lt)
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+
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+
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+ This model was trained with SFT.
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+
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+ ### Framework versions
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+
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+ - TRL: 0.24.0
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+ - Transformers: 5.2.0
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+ - Pytorch: 2.10.0
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+ - Datasets: 4.3.0
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+ - Tokenizers: 0.22.2
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+
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+ ## Citations
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+
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+
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+
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+ Cite TRL as:
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+
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+ ```bibtex
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+ @misc{vonwerra2022trl,
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+ title = {{TRL: Transformer Reinforcement Learning}},
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+ author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
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+ year = 2020,
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+ journal = {GitHub repository},
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+ publisher = {GitHub},
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+ howpublished = {\url{https://github.com/huggingface/trl}}
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+ }
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+ ```
adapter_config.json ADDED
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+ {
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+ "alora_invocation_tokens": null,
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+ "alpha_pattern": {},
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+ "arrow_config": null,
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+ "auto_mapping": {
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+ "base_model_class": "LlamaForCausalLM",
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+ "parent_library": "transformers.models.llama.modeling_llama",
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+ "unsloth_fixed": true
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+ },
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+ "base_model_name_or_path": "unsloth/meta-llama-3.1-8b-instruct-bnb-4bit",
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+ "bias": "none",
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+ "corda_config": null,
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+ "ensure_weight_tying": false,
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+ "eva_config": null,
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+ "exclude_modules": null,
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+ "fan_in_fan_out": false,
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+ "inference_mode": true,
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+ "init_lora_weights": true,
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+ "layer_replication": null,
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+ "layers_pattern": null,
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+ "layers_to_transform": null,
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+ "loftq_config": {},
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+ "lora_alpha": 32,
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+ "lora_bias": false,
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+ "lora_dropout": 0.0,
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+ "megatron_config": null,
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+ "megatron_core": "megatron.core",
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+ "modules_to_save": null,
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+ "peft_type": "LORA",
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+ "peft_version": "0.18.1",
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+ "qalora_group_size": 16,
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+ "r": 16,
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+ "rank_pattern": {},
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+ "revision": null,
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+ "target_modules": [
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+ "up_proj",
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+ "v_proj",
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+ "gate_proj",
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+ "down_proj",
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+ "k_proj",
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+ "o_proj",
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+ ],
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+ "target_parameters": null,
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+ "task_type": "CAUSAL_LM",
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+ "trainable_token_indices": null,
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+ "use_dora": false,
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+ "use_qalora": false,
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+ "use_rslora": false
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+ }
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+ {{- bos_token }}
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+ {%- if custom_tools is defined %}
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+ {%- set tools = custom_tools %}
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+ {%- endif %}
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+ {%- if not tools_in_user_message is defined %}
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+ {%- set tools_in_user_message = true %}
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+ {%- endif %}
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+ {%- if not date_string is defined %}
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+ {%- set date_string = "26 Jul 2024" %}
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+ {%- endif %}
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+ {%- if not tools is defined %}
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+ {%- set tools = none %}
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+ {%- endif %}
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+
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+ {#- This block extracts the system message, so we can slot it into the right place. #}
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+ {%- if messages[0]['role'] == 'system' %}
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+ {%- set system_message = messages[0]['content']|trim %}
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+ {%- set messages = messages[1:] %}
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+ {%- else %}
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+ {%- set system_message = "" %}
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+ {%- endif %}
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+
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+ {#- System message + builtin tools #}
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+ {{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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+ {%- if builtin_tools is defined or tools is not none %}
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+ {{- "Environment: ipython\n" }}
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+ {%- endif %}
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+ {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
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+ {%- endif %}
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+ {{- "Cutting Knowledge Date: December 2023\n" }}
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+ {{- "Today Date: " + date_string + "\n\n" }}
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+ {%- if tools is not none and not tools_in_user_message %}
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+ {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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+ {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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+ {{- "Do not use variables.\n\n" }}
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+ {%- for t in tools %}
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+ {{- t | tojson(indent=4) }}
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+ {{- "\n\n" }}
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+ {%- endfor %}
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+ {%- endif %}
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+ {{- system_message }}
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+ {{- "<|eot_id|>" }}
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+
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+ {#- Custom tools are passed in a user message with some extra guidance #}
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+ {%- if tools_in_user_message and not tools is none %}
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+ {#- Extract the first user message so we can plug it in here #}
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+ {%- if messages | length != 0 %}
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+ {%- set first_user_message = messages[0]['content']|trim %}
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+ {%- set messages = messages[1:] %}
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+ {%- else %}
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+ {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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+ {%- endif %}
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+ {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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+ {{- "Given the following functions, please respond with a JSON for a function call " }}
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+ {{- "with its proper arguments that best answers the given prompt.\n\n" }}
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+ {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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+ {{- "Do not use variables.\n\n" }}
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+ {%- for t in tools %}
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+ {{- t | tojson(indent=4) }}
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+ {{- "\n\n" }}
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+ {%- endfor %}
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+ {{- first_user_message + "<|eot_id|>"}}
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+ {%- endif %}
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+
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+ {%- for message in messages %}
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+ {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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+ {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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+ {%- elif 'tool_calls' in message %}
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+ {%- if not message.tool_calls|length == 1 %}
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+ {{- raise_exception("This model only supports single tool-calls at once!") }}
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+ {%- endif %}
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+ {%- set tool_call = message.tool_calls[0].function %}
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+ {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
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+ {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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+ {{- "<|python_tag|>" + tool_call.name + ".call(" }}
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+ {%- for arg_name, arg_val in tool_call.arguments | items %}
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+ {{- arg_name + '="' + arg_val + '"' }}
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+ {%- if not loop.last %}
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+ {{- ", " }}
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+ {%- endif %}
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+ {%- endfor %}
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+ {{- ")" }}
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+ {%- else %}
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+ {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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+ {{- '{"name": "' + tool_call.name + '", ' }}
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+ {{- '"parameters": ' }}
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+ {{- tool_call.arguments | tojson }}
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+ {{- "}" }}
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+ {%- endif %}
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+ {%- if builtin_tools is defined %}
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+ {#- This means we're in ipython mode #}
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+ {{- "<|eom_id|>" }}
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+ {%- else %}
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+ {{- "<|eot_id|>" }}
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+ {%- endif %}
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+ {%- elif message.role == "tool" or message.role == "ipython" %}
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+ {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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+ {%- if message.content is mapping or message.content is iterable %}
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+ {{- message.content | tojson }}
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+ {%- else %}
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+ {{- message.content }}
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+ {%- endif %}
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+ {{- "<|eot_id|>" }}
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+ {%- endif %}
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+ {%- endfor %}
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+ {%- if add_generation_prompt %}
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+ {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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+ {%- endif %}
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