Instructions to use dman1011/recall-honcho-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dman1011/recall-honcho-8b with Transformers:

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

pipe = pipeline("text-generation", model="dman1011/recall-honcho-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dman1011/recall-honcho-8b")
model = AutoModelForCausalLM.from_pretrained("dman1011/recall-honcho-8b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dman1011/recall-honcho-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dman1011/recall-honcho-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dman1011/recall-honcho-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dman1011/recall-honcho-8b

SGLang

How to use dman1011/recall-honcho-8b 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 "dman1011/recall-honcho-8b" \
    --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": "dman1011/recall-honcho-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "dman1011/recall-honcho-8b" \
        --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": "dman1011/recall-honcho-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dman1011/recall-honcho-8b with Docker Model Runner:
```
docker model run hf.co/dman1011/recall-honcho-8b
```

recall-honcho-8b — a local Honcho deriver (explicit conclusion extraction)

A Qwen/Qwen3-8B fine-tune specialised for the explicit conclusion-derivation step of Honcho: given a target peer and their chat turns, emit atomic, self-contained, correctly-attributed facts as Honcho-schema JSON. Independent, self-hosted re-creation of the role Plastic Labs' (closed) Neuromancer XR plays in production Honcho. Not affiliated with Plastic Labs.

Results

Held-out validation (298 examples, 4% split), bf16 LoRA, 2 epochs:

Metric	Value
Eval loss	0.201
Eval token accuracy	93.8%
Train loss (final)	0.197
Loss curve	2.03 → 0.13 over 896 steps

The model reliably reproduces the exact {"explicit":[{"content":...}]} schema with correct attribution, absolute dates, and atomic facts. Worked example (held-out):

Input  : message about buying a secondhand Eames lounge chair
Output : {"explicit": [
  {"content": "dmitri bought a secondhand Eames lounge chair from a Facebook listing in Williamsburg"},
  {"content": "dmitri paid $900 for the secondhand Eames lounge chair"},
  {"content": "dmitri rented a Zipcar to haul the Eames lounge chair back, costing another $80"} ]}

LoCoMo benchmark (base Qwen3-8B vs this model, through Honcho's dialectic pipeline) — to be added. Reference points from Plastic Labs' blog (different/private data, not directly comparable): base Qwen3-8B 69.6, Claude 4 Sonnet 80.0, Neuromancer XR 86.9.

Training

Base: Qwen/Qwen3-8B
Data: 7,160 synthetic SFT examples, gold labels distilled from Claude Opus 4.8 (frontier teacher), 15 balanced life-domains, rendered through Honcho's own prompt-builder and schema-validated against PromptRepresentation. Distilling a compact deriver from a frontier model is the core idea.
Method: bf16 LoRA (rank 32, alpha 32), 2 epochs, lr 2e-4 cosine, completion-only loss.
Hardware: NVIDIA RTX PRO 6000 Blackwell (sm_120), ~56 min.

Prompt / I-O contract

Input = Honcho's minimal_deriver_prompt(peer_id, messages) verbatim (non-thinking). Output = JSON validated by PromptRepresentation:

{"explicit": [{"content": "alice is training for a half-marathon scheduled for October 2026"}]}

Rules: atomic · self-contained · absolute dates · correct third-party attribution · explicit-only (no speculation) · {"explicit": []} when nothing is stated.

How to use with Honcho

Serve (vLLM/Ollama), expose via your gateway (e.g. litellm route recall-honcho-8b), then point Honcho's deriver at it in config.toml:

[deriver.model_config]
transport = "openai"
model = "recall-honcho-8b"
[deriver.model_config.overrides]
base_url = "http://<gateway>/v1"
api_key_env = "LLM_OPENAI_API_KEY"

Keep the dialectic/generation step on a larger model.

Files

Merged bf16 model (root) — servable standalone.
lora-adapter/ — the LoRA adapter alone (apply onto Qwen/Qwen3-8B).

Limitations

Explicit level only (not deductive/inductive/abductive — Honcho's dreamer).
English only; trained on privacy-safe synthetic data (frontier-distilled).
Optimised for Honcho's exact prompt; off-format prompts may degrade output discipline.