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Model: ermiaazarkhalili/FastContext-4B-SFT_base-SFT-Fable5 Source: Original Platform
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README.md
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README.md
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---
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base_model:
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- microsoft/FastContext-1.0-4B-SFT
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- unsloth
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- lora
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- trl
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- sft
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---
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# FastContext-4B-SFT_base-SFT-Fable5
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A LoRA fine-tune of `microsoft/FastContext-1.0-4B-SFT` (no longer available on the Hub), supervised fine-tuned on `ermiaazarkhalili/Fable-5-Complete-2M-Clean` (private).
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| | |
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| --- | --- |
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| **Base model** | `microsoft/FastContext-1.0-4B-SFT` (no longer available on the Hub) |
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| **Architecture** | `Qwen3ForCausalLM` |
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| **Parameters** | 4.0B |
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| **Training data** | `ermiaazarkhalili/Fable-5-Complete-2M-Clean` (private) |
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| **Method** | LoRA supervised fine-tuning via [Unsloth](https://github.com/unslothai/unsloth) + [TRL](https://github.com/huggingface/trl) |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "ermiaazarkhalili/FastContext-4B-SFT_base-SFT-Fable5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, dtype='auto', device_map='auto')
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messages = [{"role": "user", "content": "Explain gradient checkpointing in two sentences."}]
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inputs = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors='pt'
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).to(model.device)
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outputs = model.generate(inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Training configuration
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| Setting | Value |
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| --- | --- |
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| LoRA rank (r) | 16 |
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| LoRA alpha | 16 |
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| Learning rate | 0.0002 |
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| Epochs | 2 |
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| Effective batch size | 8 (2 x 4 grad accum) |
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| Max sequence length | 4096 |
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| Base precision | 4-bit (QLoRA) |
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| Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
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## Observed training loss
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Measured from our SLURM logs for this configuration. These are training-loss
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observations only — no downstream benchmark evaluation has been run on this
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model, so they should not be read as a quality claim.
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| SLURM job | Steps | First loss | Final loss |
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| --- | --- | --- | --- |
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| `53225528` | 94,254 | 1.1628 | 0.8690 |
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## Limitations
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- No benchmark evaluation has been run on this checkpoint. The only reported
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numbers are training-loss observations.
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- Inherits the biases, knowledge cutoff and failure modes of the base model.
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- Fine-tuned on a single instruction-following dataset; behaviour outside that
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distribution is untested.
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- LoRA adapters were merged into the base weights, so the merged model cannot
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be detached from this fine-tune.
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## Reproducing
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Trained by `notebooks/fable_distillation_fastcontext-4b-sft_fable_unsloth.ipynb`, executed non-interactively with
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papermill on a SLURM H100 partition (Unsloth + TRL, LoRA).
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---
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*Card generated from the training run's own configuration and logs by*
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*`scripts/generate_hub_model_card.py`.*
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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 + '\n\n' }}
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{%- endif %}
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{{- "# 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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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first 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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{{- 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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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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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": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 9728,
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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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"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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"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": 262144,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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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_theta": 5000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.5.0",
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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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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.5.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:caf4960e1da422dbbb3c7f694ad66e2ecb1734a8d3c88c2d4d54fa5f1a0dc3dc
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size 8044982080
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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 sha256:476870a1f2fb6f6a2759a6ede2383bf9d5d738f17844563b65c91965b722ae09
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size 11422924
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [],
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"is_local": true,
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"model_max_length": 262144,
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"pad_token": "<|endoftext|>",
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"padding_side": "left",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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