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Model: steven0226/qwen2.5-1.5b-wordle-grpo-merged Source: Original Platform
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*.7z filter=lfs diff=lfs merge=lfs -text
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84
README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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pipeline_tag: text-generation
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tags:
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- reinforcement-learning
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- grpo
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- agentic-rl
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- multi-turn
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- wordle
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- merged-model
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language:
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- en
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---
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# Qwen2.5-1.5B Wordle GRPO merged full model
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## Repository role
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This repository contains the historical **merged full model** counterpart of the Wordle GRPO
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LoRA adapter. It does not require a separately attached LoRA adapter for loading. The base
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repository identity is `Qwen/Qwen2.5-1.5B-Instruct`; the current `model.safetensors` LFS SHA-256 is
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`b6c55086e798e1f62e6d970f07ee97ab39c1e0af3ee4b6ecdb2a349e485087af`.
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The repository relationship is supported by historical documentation, subject to the lineage
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limits below. It is not a cryptographically complete adapter-to-merged derivation record.
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## Evaluation
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The published 463-word result originates from the adapter evaluation. No independent 463-word evaluation was run against these merged bytes, so the adapter result is not an independent replication of merged-model performance. The aggregate adapter result is included here only to
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document the paired project evidence without inventing a separate merged-model result.
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| Measure | Base | Tuned LoRA adapter evidence |
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|---|---:|---:|
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| Wins | 0/463 | 13/463 = 2.81% |
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| Wilson 95% CI | 0.00%–0.82% | 1.65%–4.74% |
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| Protocol adherence | 0% | 2749/2753 = 99.85% |
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| Legal actions | 0% | 2748/2753 = 99.82% |
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The paired discordance was 0 base-only wins and 13 tuned-only wins. The two-sided exact paired
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McNemar result is `0.000244140625`; treating the interim and final evaluations as two looks gives
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the conservative Bonferroni-adjusted value `0.00048828125`.
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The adapter evaluation also recorded 1340/2290 absent-letter reuses and 1119/2290 broken green
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positions on turns with information.
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**Protocol learning succeeded; strategy learning remained limited; this is not a practical Wordle solver.** The 2.81% adapter win rate must not be presented as an independently measured
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merged-model win rate, practical capability, or general RL superiority.
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## Evidence links
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- Immutable research/evidence source:
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https://github.com/kuotunyu/agentic-rl-wordle/commit/1a077a45e309594e5bb43743a8b84d89155595d4
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- Stable source release URL:
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https://github.com/kuotunyu/agentic-rl-wordle/releases/tag/v1.0.0
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- Aggregate report: `results/full_463_report.json`
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- Recomputed analysis: `results/full_463_analysis.json`
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- Public claim mapping: `docs/claim-matrix.md`
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## Limitations
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- Only aggregate 463-game evidence is committed; full per-episode records are unavailable.
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- The historical GPU environment is not bit-for-bit reconstructable.
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- The exact upstream Qwen commit was not preserved.
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- The adapter-to-merged command and manifest were not preserved, and the historical record lacks
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an end-to-end run→code→prompt→bundle→model cryptographic chain. The relationship is documentary lineage, not complete cryptographic proof.
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- The cfreshman word lists are fetch-only and have no explicit license. Apache-2.0 does not license the cfreshman word lists.
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- Representative transcripts are illustrations, not a complete raw evaluation corpus.
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- No independent evaluation of these merged bytes is available; do not infer byte-level
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equivalence or independently replicated performance from the documentary relationship.
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## Loading
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This repository is intended to load as a full causal language model and does not need a separate
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PEFT adapter attachment. Because the exact historical upstream base revision and merge manifest
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are unavailable, do not claim a complete cryptographic reconstruction of the merge.
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```python
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from transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(
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"steven0226/qwen2.5-1.5b-wordle-grpo-merged"
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)
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```
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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61
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"dtype": "bfloat16",
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_type": "default"
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.12.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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14
generation_config.json
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generation_config.json
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{
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"top_p": 0.8,
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"transformers_version": "5.12.1"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 3087467144
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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tokenizer_config.json
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"is_local": false,
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}
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