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Model: renaudb1999/le-harnais-ft-agentworld-1b Source: Original Platform
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38
.gitattributes
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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ft-agentworld-1b.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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ft-agentworld-1b.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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31
README.md
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31
README.md
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---
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license: llama3.2
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base_model: meta-llama/Llama-3.2-1B-Instruct
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library_name: transformers
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tags: [le-harnais, hero]
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---
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# le-harnais / ft-agentworld-1b
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World-model student distilled from Qwen-AgentWorld-35B; Mac-runnable tool-outcome predictor.
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- **Base model:** [`meta-llama/Llama-3.2-1B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) — **Built with Llama**; Llama Community License applies.
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- **Class:** `hero`
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- **Training data:** datasets/agentworld_distill_train.jsonl (160 ex; teacher=Qwen-AgentWorld-35B-A3B)
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- **Headline:** token-F1 0.826 / OBSERVATION hit-rate 60% vs teacher (n=40)
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## Reproduce
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```sh
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BASE=meta-llama/Llama-3.2-1B-Instruct OUT=refs/llm-jepa/ft-agentworld-1b tools/distill_agentworld.sh
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```
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Full recipe, datasets, and eval commands: see `docs/REPRODUCE.md` in the
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[le-harnais distribution]. Provenance & license: `docs/PROVENANCE.md`.
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## Formats in this repo
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- `*.safetensors` — bf16 inference weights (serve with `transformers` or le-harnais `lh-serve`/candle).
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- `*.Q4_K_M.gguf` — portable 4-bit quant (run via `ollama` / `llama.cpp`; Mac-friendly).
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- `*.Q8_0.gguf` — higher-fidelity 8-bit quant (hero models).
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> Orchestration amplifies a capable generator; it does not create competence.
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93
chat_template.jinja
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93
chat_template.jinja
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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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{%- if strftime_now is defined %}
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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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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{#- 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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{#- System message #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\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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{#- 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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{%- 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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{{- '<|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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{{- "<|eot_id|>" }}
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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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39
config.json
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39
config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.55.2",
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"use_cache": false,
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"vocab_size": 128267
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}
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3
ft-agentworld-1b.Q4_K_M.gguf
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3
ft-agentworld-1b.Q4_K_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:af4e9c4e40185e36d6c82086508030f9dc6fd881b8130e83241f270c0c872d30
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size 807712832
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3
ft-agentworld-1b.Q8_0.gguf
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3
ft-agentworld-1b.Q8_0.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:b0be146e0bf777eac500e10343f3f165708cca06e685f1c0c1524b41d9379662
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size 1321106816
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12
generation_config.json
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12
generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.55.2"
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}
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3
model.safetensors
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3
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:20472b562986e67242aa33d177bccdcf543d00b3237d7e7ac38c8a6c6db2588d
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size 2471690664
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96
special_tokens_map.json
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96
special_tokens_map.json
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{
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"additional_special_tokens": [
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{
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"content": "<|predictor_1|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "<|predictor_2|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "<|predictor_3|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "<|predictor_4|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "<|predictor_5|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "<|predictor_6|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "<|predictor_7|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "<|predictor_8|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "<|predictor_9|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "<|predictor_10|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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{
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"content": "<|perception|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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],
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"bos_token": {
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"content": "<|begin_of_text|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
|
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"content": "<|eot_id|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<|eot_id|>"
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}
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3
tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:78f692c83c9abf03141aa73f056d2d200a33d01f2b482b70b98c5438a48736a6
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size 17212032
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2164
tokenizer_config.json
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2164
tokenizer_config.json
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File diff suppressed because it is too large
Load Diff
64
trainer_state.json
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64
trainer_state.json
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{
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"best_global_step": null,
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"best_metric": null,
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"best_model_checkpoint": null,
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"epoch": 3.0,
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"eval_steps": 500,
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"global_step": 30,
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
|
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"log_history": [
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{
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"epoch": 1.0,
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"grad_norm": 18.375,
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||||
"learning_rate": 7e-06,
|
||||
"loss": 1.9017,
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"step": 10
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},
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{
|
||||
"epoch": 2.0,
|
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"grad_norm": 6.09375,
|
||||
"learning_rate": 3.6666666666666666e-06,
|
||||
"loss": 0.5422,
|
||||
"step": 20
|
||||
},
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{
|
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"epoch": 3.0,
|
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"grad_norm": 6.375,
|
||||
"learning_rate": 3.3333333333333335e-07,
|
||||
"loss": 0.4105,
|
||||
"step": 30
|
||||
},
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{
|
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"epoch": 3.0,
|
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"step": 30,
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"total_flos": 2869924661821440.0,
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"train_loss": 0.9514639854431153,
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"train_runtime": 44.0068,
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"train_samples_per_second": 10.907,
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"train_steps_per_second": 0.682
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}
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],
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"logging_steps": 10,
|
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"max_steps": 30,
|
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"num_input_tokens_seen": 0,
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"num_train_epochs": 3,
|
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"save_steps": 10,
|
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"stateful_callbacks": {
|
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"TrainerControl": {
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"args": {
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"should_epoch_stop": false,
|
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"should_evaluate": false,
|
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"should_log": false,
|
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"should_save": true,
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"should_training_stop": true
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},
|
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"attributes": {}
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||||
}
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},
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"total_flos": 2869924661821440.0,
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||||
"train_batch_size": 4,
|
||||
"trial_name": null,
|
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"trial_params": null
|
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}
|
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Block a user