license, language, tags, base_model, pipeline_tag

license	language	tags	base_model	pipeline_tag
apache-2.0	en	email cold-outreach text-generation qwen3 fine-tuned	Qwen/Qwen3-0.6B	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)

# 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

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