Instructions to use gittensor-model-hub/Spark-Hermes-3.8-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gittensor-model-hub/Spark-Hermes-3.8-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="gittensor-model-hub/Spark-Hermes-3.8-27B")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("gittensor-model-hub/Spark-Hermes-3.8-27B", device_map="auto")

HERMES

How to use gittensor-model-hub/Spark-Hermes-3.8-27B with HERMES:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gittensor-model-hub/Spark-Hermes-3.8-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gittensor-model-hub/Spark-Hermes-3.8-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gittensor-model-hub/Spark-Hermes-3.8-27B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gittensor-model-hub/Spark-Hermes-3.8-27B

SGLang

How to use gittensor-model-hub/Spark-Hermes-3.8-27B 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 "gittensor-model-hub/Spark-Hermes-3.8-27B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gittensor-model-hub/Spark-Hermes-3.8-27B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "gittensor-model-hub/Spark-Hermes-3.8-27B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gittensor-model-hub/Spark-Hermes-3.8-27B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gittensor-model-hub/Spark-Hermes-3.8-27B with Docker Model Runner:
```
docker model run hf.co/gittensor-model-hub/Spark-Hermes-3.8-27B
```

Spark-Hermes-3.8-27B

Hermes × Qwen3.8-27B co-trained agent model from Gittensor Model Hub.

This repo is the model card first. Weights are not uploaded yet. They will land here.

Built to pair with SparkInfer on RTX 5090 and RTX PRO 6000: few SOTA models, optimized all the way, already 2–3× llama.cpp.

Item	Value
Base	`Qwen/Qwen3.8-27B`
Training	Co-trained with Hermes (agent / tool-use stack)
Role	Agent model — tools, long-running work, steerability
Serve	SparkInfer on Blackwell (RTX 5090, RTX PRO 6000)
Status	README / card only — weights coming
License	Apache 2.0 (same as the Qwen3.8-27B base)

What this is

Qwen3.8-27B is a dense 27B vision-language model (hybrid Gated DeltaNet + Gated Attention, native 262,144 context). Hermes is the agent post-train lineage: function calling, structured output, multi-turn control.

Spark-Hermes-3.8-27B co-trains those two: Qwen3.8-27B capability with Hermes agent behavior — for tools, JSON, and long-horizon work on a single workstation GPU.

Sister checkpoint on this hub (already shipped): Qwen3.8-27B-NVFP4-RTX5090 — NVIDIA ModelOpt NVFP4 of the Qwen3.8-27B base for RTX 5090 (full 256k on 32 GB).

Status

Weights are not in this repository yet. This page is the card so the name, lineage, and intended use are public while training / export finishes.

When weights land, this README will add serve commands (SparkInfer first, then vLLM) and eval.

Intended use

Agentic assistants (tools, function calling, structured output)
Long-running research / coding / worker loops
Local serve on RTX 5090 and RTX PRO 6000 via SparkInfer

Not a drop-in replacement for the NVFP4 base checkpoint until weights are published here.

Lineage

Qwen/Qwen3.8-27B  ×  Hermes (agent co-train)
        └── Spark-Hermes-3.8-27B   ← this repo (card first)

License

Apache 2.0, same as Qwen/Qwen3.8-27B.

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for gittensor-model-hub/Spark-Hermes-3.8-27B

Base model

Qwen/Qwen3.8-27B

Finetuned

(124)

this model