| | --- |
| | library_name: transformers |
| | license: cc-by-nc-4.0 |
| | base_model: ZeroAgency/zero-llama-3.1-8b-beta6 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - bethrezen/thinking-summary-v2 |
| | model-index: |
| | - name: outputs/zero-summary-v2-beta15 |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) |
| | <details><summary>See axolotl config</summary> |
| |
|
| | axolotl version: `0.8.0.dev0` |
| | ```yaml |
| | # zero-summary-v2-beta15 |
| | # base on 1 |
| | #adapter: lora |
| | base_model: ZeroAgency/zero-llama-3.1-8b-beta6 |
| | dataset_processes: 64 |
| | chat_template: jinja |
| | chat_template_jinja: "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n" |
| | |
| | |
| | dataset_prepared_path: ./last_run_prepared |
| | |
| | datasets: |
| | - message_property_mappings: |
| | content: content |
| | role: role |
| | path: bethrezen/thinking-summary-v2 |
| | trust_remote_code: false |
| | field_messages: conversation |
| | type: chat_template |
| | |
| | |
| | # approx 20k samples should be enough |
| | #val_set_size: 0.061 |
| | |
| | # exact duplicates are already cleaned |
| | #dataset_exact_deduplication: true |
| | |
| | gradient_accumulation_steps: 1 |
| | gradient_checkpointing: true |
| | gradient_checkpointing_kwargs: |
| | use_reentrant: false |
| | |
| | #learning_rate: 0.0001 |
| | learning_rate: 4e-5 |
| | lisa_layers_attribute: model.layers |
| | |
| | plugins: |
| | - axolotl.integrations.liger.LigerPlugin |
| | liger_rope: true |
| | liger_rms_norm: true |
| | liger_swiglu: true |
| | liger_fused_linear_cross_entropy: true |
| | |
| | load_best_model_at_end: true |
| | load_in_4bit: false |
| | load_in_8bit: false |
| | # lora_alpha: 256 |
| | # lora_dropout: 0.01 |
| | # lora_target_linear: true |
| | # lora_r: 256 |
| | |
| | lr_scheduler: cosine |
| | #max_prompt_len: 8192 |
| | mean_resizing_embeddings: false |
| | micro_batch_size: 1 |
| | num_epochs: 2 |
| | optimizer: adamw_torch_fused |
| | output_dir: ./outputs/zero-summary-v2-beta15 |
| | |
| | |
| | sample_packing_bin_size: 200 |
| | sample_packing_group_size: 100000 |
| | save_only_model: false |
| | save_safetensors: true |
| | sequence_len: 110000 |
| | min_sample_len: 1 |
| | #shuffle_merged_datasets: true |
| | skip_prepare_dataset: false |
| | strict: false |
| | train_on_inputs: false |
| | |
| | |
| | weight_decay: 0.01 |
| | wandb_project: zero-summary |
| | wandb_name: zero-summary-v2-beta15 |
| | bf16: true |
| | fp16: false |
| | tf32: false |
| | flash_attention: true |
| | |
| | save_strategy: epoch |
| | eval_strategry: epoch |
| | |
| | logging_steps: 1 |
| | save_total_limit: 5 |
| | warmup_steps: 15 |
| | sample_packing: true |
| | pad_to_sequence_len: true |
| | group_by_length: true |
| | seed: 42 |
| | data_seed: 42 |
| | |
| | deepspeed: /workspace/axolotl/deepspeed_configs/zero1_torch_compile.json |
| | log_with: wandb |
| | trust_remote_code: true |
| | use_fast_tokenizer: true |
| | special_tokens: |
| | pad_token: "<|finetune_right_pad_id|>" |
| | eos_token: <|eot_id|> |
| | ``` |
| |
|
| | </details><br> |
| |
|
| | # outputs/zero-summary-v2-beta15 |
| |
|
| | This model is a fine-tuned version of [ZeroAgency/zero-llama-3.1-8b-beta6](https://huggingface.co/ZeroAgency/zero-llama-3.1-8b-beta6) on the bethrezen/thinking-summary-v2 dataset. |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 4e-05 |
| | - train_batch_size: 1 |
| | - eval_batch_size: 1 |
| | - seed: 42 |
| | - distributed_type: multi-GPU |
| | - num_devices: 8 |
| | - total_train_batch_size: 8 |
| | - total_eval_batch_size: 8 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_steps: 15 |
| | - num_epochs: 2.0 |
| |
|
| | ### Training results |
| |
|
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.49.0 |
| | - Pytorch 2.5.1+cu124 |
| | - Datasets 3.2.0 |
| | - Tokenizers 0.21.0 |
| |
|
