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Model: claytonwang/rlcr-qwen3-8b
Source: Original Platform

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - qwen3
+  - causal-lm
+  - safetensors
+  - bf16
+---
+
+# Qwen3 Causal Language Model (BF16)
+
+This repository contains a decoder-only causal language model using the Qwen3
+architecture. The weights are stored in sharded Safetensors format using BF16
+precision.
+
+## Model details
+
+| Item | Value |
+| --- | --- |
+| Architecture | `Qwen3ForCausalLM` |
+| Model type | `qwen3` |
+| Parameters | 8,190,735,360 |
+| Weight precision | BF16 |
+| Hidden size | 4096 |
+| Layers | 36 |
+| Attention heads | 32 |
+| Key/value heads | 8 |
+| Vocabulary size | 151,936 |
+| Configured context length | 40,960 tokens |
+| Weight format | 7 sharded Safetensors files (about 5 GB each) |
+
+The tokenizer configuration currently declares a maximum length of 131,072,
+while the model configuration declares 40,960 positions. Until the longer
+context length is independently validated, use 40,960 tokens as the supported
+maximum.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "YOUR_USERNAME/YOUR_MODEL_NAME"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    attn_implementation="sdpa",
+)
+
+messages = [{"role": "user", "content": "你好，请介绍一下你自己。"}]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+with torch.inference_mode():
+    output = model.generate(**inputs, max_new_tokens=256)
+
+answer = output[0, inputs.input_ids.shape[1]:]
+print(tokenizer.decode(answer, skip_special_tokens=True))
+```
+
+BF16 weights require roughly 16 GB just for the model parameters, plus
+additional memory for inference state and the KV cache. Use a machine with
+sufficient RAM or accelerator memory. The example selects SDPA so that
+FlashAttention 2 is not required; installations with FlashAttention 2 may
+choose `attn_implementation="flash_attention_2"` instead.
+
+## Training and provenance
+
+The files supplied with this model do not identify the following information:
+
+- Base model or checkpoint
+- Training or fine-tuning method
+- Training datasets
+- Evaluation results
+- Model author or organization
+
+The uploader should add these details before publishing so users can assess the
+model's capabilities, limitations, and provenance.
+
+## License
+
+No license information was included with the supplied model files. The uploader
+must verify the licenses of the base model, training data, and resulting weights
+and add the correct license before making the repository public. Absence of a
+license does not grant permission to use, modify, or redistribute the model.
+
+## Limitations
+
+No benchmark, safety, bias, or long-context evaluation results were supplied.
+Do not rely on this model for high-impact decisions without appropriate testing
+and human review.