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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,36 @@
+---
+license: apache-2.0
+base_model: huizimao/gpt-oss-120b-uncensored-mxfp4
+tags:
+- mlx
+pipeline_tag: text-generation
+library_name: mlx
+---
+
+# huizimao-gpt-oss-120b-uncensored-mxfp4-q6-hi-mlx
+
+This model [huizimao-gpt-oss-120b-uncensored-mxfp4-q6-hi-mlx](https://huggingface.co/huizimao-gpt-oss-120b-uncensored-mxfp4-q6-hi-mlx) was
+converted to MLX format from [huizimao/gpt-oss-120b-uncensored-mxfp4](https://huggingface.co/huizimao/gpt-oss-120b-uncensored-mxfp4)
+using mlx-lm version **0.26.3**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("huizimao-gpt-oss-120b-uncensored-mxfp4-q6-hi-mlx")
+
+prompt = "hello"
+
+if tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True
+    )
+
+response = generate(model, tokenizer, prompt=prompt, verbose=True)
+```

config.json

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+{
+    "architectures": [
+        "GptOssForCausalLM"
+    ],
+    "attention_bias": true,
+    "attention_dropout": 0.0,
+    "eos_token_id": 200002,
+    "experts_per_token": 4,
+    "head_dim": 64,
+    "hidden_act": "silu",
+    "hidden_size": 2880,
+    "initial_context_length": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 2880,
+    "layer_types": [
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention",
+        "sliding_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 131072,
+    "model_type": "gpt_oss",
+    "num_attention_heads": 64,
+    "num_experts_per_tok": 4,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "num_local_experts": 128,
+    "output_router_logits": false,
+    "pad_token_id": 199999,
+    "quantization": {
+        "group_size": 32,
+        "bits": 6
+    },
+    "quantization_config": {
+        "group_size": 32,
+        "bits": 6
+    },
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": {
+        "beta_fast": 32.0,
+        "beta_slow": 1.0,
+        "factor": 32.0,
+        "original_max_position_embeddings": 4096,
+        "rope_type": "yarn",
+        "truncate": false
+    },
+    "rope_theta": 150000,
+    "router_aux_loss_coef": 0.9,
+    "sliding_window": 128,
+    "swiglu_limit": 7.0,
+    "tie_word_embeddings": false,
+    "transformers_version": "4.55.0.dev0",
+    "use_cache": true,
+    "vocab_size": 201088
+}

generation_config.json

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+  "do_sample": true,
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