初始化项目，由ModelHub XC社区提供模型

Model: CharlieGreenman/email-qwen3-0.6b
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- email
+- cold-outreach
+- text-generation
+- qwen3
+- fine-tuned
+base_model: Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
+---
+
+# Email-Qwen3-0.6B — Fine-tuned for Email Generation
+
+A fine-tuned Qwen3 0.6B model specialized in generating professional emails from simple prompts. Trained on 130k curated email examples with 5 rounds of rejection sampling alignment.
+
+## Model Details
+
+- **Base model:** Qwen/Qwen3-0.6B
+- **Training:** SFT on 130k prompt-email pairs + 5 rounds rejection sampling fine-tuning
+- **Quantized version:** Q4_K_M (378MB) available for local inference via llama.cpp
+- **Use case:** Cold outreach, thank-you, request, apology, invitation, congratulations, and 10+ other email types
+
+## Usage
+
+### With llama.cpp (recommended)
+
+```bash
+# Download the GGUF quantized version
+# Start the server
+llama-server -m email-qwen3-06b-q4_k_m.gguf --host 127.0.0.1 --port 8081 -c 2048
+
+# Generate an email
+curl http://127.0.0.1:8081/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [
+      {"role": "system", "content": "You are an email writing assistant. Write a polished email body for the given request."},
+      {"role": "user", "content": "Cold outreach to the CTO at Stripe about our developer tools platform"}
+    ],
+    "max_tokens": 256,
+    "temperature": 0.7
+  }'
+```
+
+### With Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("CharlieGreenman/email-qwen3-0.6b")
+tokenizer = AutoTokenizer.from_pretrained("CharlieGreenman/email-qwen3-0.6b")
+
+messages = [
+    {"role": "system", "content": "You are an email writing assistant. Write a polished email body for the given request."},
+    {"role": "user", "content": "Thank Sarah for helping with the presentation last week"},
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
+print(tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))
+```
+
+## Tips for Best Results
+
+- **One paragraph at a time:** This model performs best when asked to generate individual paragraphs rather than full multi-paragraph emails. Generate each paragraph with a focused prompt, then assemble.
+- **Keep prompts specific:** Include the recipient's name, company, role, and topic for better personalization.
+- **Use best-of-N:** Generate 3-5 variants and pick the best one. Small models benefit significantly from selection.
+- **Temperature 0.7-0.8** works well for email generation.
+
+## Training Data
+
+- 50,000 diverse email prompts across 17 email types
+- 34,055 high-quality prompt-email pairs (scored 80+ by our quality scorer)
+- 96,034 section-level examples (individual email paragraphs)
+- 5 rounds of rejection sampling using best-of-5 selection with quality scoring
+
+## Supported Email Types
+
+Cold outreach, follow-up, newsletter, transactional, welcome, personal, request, meeting, FYI, thank-you, confirmation, apology, introduction, invitation, deadline, congratulations, and freeform.
+
+## Limitations
+
+- Best for common email types; may struggle with unusual or highly creative prompts
+- Generates email body text; subject lines should be handled separately
+- Small model (0.6B) — quality improves significantly with best-of-N selection and post-processing
+- May occasionally hallucinate company names or statistics
+
+## License
+
+Apache 2.0