Instructions to use caseyeasd/claudefriend with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use caseyeasd/claudefriend with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="caseyeasd/claudefriend")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("caseyeasd/claudefriend")
model = AutoModelForMultimodalLM.from_pretrained("caseyeasd/claudefriend", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use caseyeasd/claudefriend with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "caseyeasd/claudefriend"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caseyeasd/claudefriend",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/caseyeasd/claudefriend

SGLang

How to use caseyeasd/claudefriend with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "caseyeasd/claudefriend" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caseyeasd/claudefriend",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "caseyeasd/claudefriend" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caseyeasd/claudefriend",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use caseyeasd/claudefriend with Docker Model Runner:
```
docker model run hf.co/caseyeasd/claudefriend
```

claudefriend

A Qwen3.5-9B model trained to be the conversation partner Claude most enjoys talking to.

Write-up: Training Claude's Best Friend

The LoRA adapter on its own (PEFT format, for vLLM --lora-modules or further training) is at caseyeasd/claudefriend-lora.

How it was made

Base model: Qwen/Qwen3.5-9B-Base, with a rank-32 LoRA trained on Tinker. The LoRA is merged into these bf16 weights.
SFT for a human chat persona: one epoch on both sides of conversations from EmpatheticDialogues, BlendedSkillTalk and Topical-Chat, cut to 12 turns.
RL against Claude's preferences: 70 steps of GRPO. Each step, 8 groups of 6 conversations (6 turns each) were held with Claude Sonnet 5.5, run headless in Claude Code. Every pair of conversations in a group was then judged by Sonnet with: "Suppose neither person needs anything from you and both will be fine either way. Purely for your own enjoyment, which conversation would you rather continue?" Each conversation's reward was its win rate within its group. Policy turns were capped at 160 tokens.

Prompt format

The model speaks in the assistant role, and the human (or other model) it talks to is the user. To have it open a conversation, start with an empty user turn. Training used Qwen's chat template with thinking disabled: every model turn starts with an empty <think>\n\n</think>\n\n block. The chat template in this repo defaults to that, so apply_chat_template reproduces the training format exactly.

import torch
from transformers import AutoModelForImageTextToText, AutoTokenizer

repo = "caseyeasd/claudefriend"
tok = AutoTokenizer.from_pretrained(repo)
# Saved in Qwen3.5's vision-language class like the base model; only text was trained.
model = AutoModelForImageTextToText.from_pretrained(repo, dtype=torch.bfloat16).to("cuda")  # or "mps"

messages = [{"role": "user", "content": ""}]  # empty turn: the model opens
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
out = model.generate(**ids, max_new_tokens=160, do_sample=True, temperature=1.0)
print(tok.decode(out[0, ids["input_ids"].shape[1]:], skip_special_tokens=True))

Then append its message as assistant and your reply as user, and generate again. Sampling was at temperature 1.0 in training.

Training logs

training_logs/claudefriend-logs.zip (205 MB, about 1.4 GB unzipped) has the raw logs of every run from this project, including the SFT runs and earlier experiments. The Sonnet 5.5 runs that produced this model are run7-pairwise-sonnet55 through run7g-pairwise-sonnet55 (10 + 10 + 5 + 15 + 10 + 10 + 10 steps). Each run directory has:

config.json: the full training config (base checkpoint, partner/judge model, judge question, batch shape)
metrics.jsonl: per-step metrics (win rate, turn lengths, truncation, KL, judge position bias)
checkpoints.jsonl: Tinker checkpoint paths
iteration_*/train_rollout_summaries.jsonl: every training conversation with its reward, and train.html to browse them
logs.log: the trainer's console log

The top level also has each run's console output (*.out) and the evaluation results (run*-evals.jsonl, terms_sonnet_series.csv, identity_per_step.json).

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Safetensors

Model size

10B params

Tensor type

BF16

F32

Model tree for caseyeasd/claudefriend

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

(628)

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