Instructions to use WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B with Transformers:

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

pipe = pipeline("text-generation", model="WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B")
model = AutoModelForCausalLM.from_pretrained("WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B

SGLang

How to use WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B 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 "WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B" \
    --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": "WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B",
		"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 "WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B" \
        --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": "WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/Llama3.2-Hermes.Dolphin.Coder-1B
```

gss1147 commited on Mar 23

Commit

3c0868e

verified ·

1 Parent(s): 6c24a9c

Update README.md

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@@ -1,54 +1,70 @@
 ---
 base_model:
-- artificialguybr/LLAMA-3.2-1B-OpenHermes2.5
-- dphn/Dolphin3.0-Llama3.2-1B
-- meta-llama/Llama-3.2-1B-Instruct
 library_name: transformers
 tags:
-- mergekit
-- merge
 datasets:
-- OpenCoder-LLM/opc-sft-stage1
-- OpenCoder-LLM/opc-sft-stage2
-- microsoft/orca-agentinstruct-1M-v1
-- microsoft/orca-math-word-problems-200k
-- NousResearch/hermes-function-calling-v1
-- AI-MO/NuminaMath-CoT
-- AI-MO/NuminaMath-TIR
-- allenai/tulu-3-sft-mixture
-- HuggingFaceTB/smoltalk
-- m-a-p/CodeFeedback-Filtered-Instruction
-- m-a-p/Code-Feedback
-- teknium/OpenHermes-2.5
 ---
-# merged_20260322_234544
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
-### Models Merged
-The following models were included in the merge:
-* dphn-Dolphin3.0-Llama3.2-1B
-* artificialguybr-LLAMA-3.2-1B-OpenHermes2.5
-### Configuration
-The following YAML configuration was used to produce this model:
 ```yaml
 merge_method: slerp
-base_model: X:\Amalgamation AI Universal GUI\_prepared_models\artificialguybr-LLAMA-3.2-1B-OpenHermes2.5_1a7ec6f29c
 models:
-- model: X:\Amalgamation AI Universal GUI\_prepared_models\dphn-Dolphin3.0-Llama3.2-1B_a8e1ced6b4
-  parameters:
-    weight: 1.0
 dtype: float32
 parameters:
-  t: 0.5
-```

 ---
+license: other
 base_model:
+  - artificialguybr/LLAMA-3.2-1B-OpenHermes2.5
+  - dphn/Dolphin3.0-Llama3.2-1B
+  - meta-llama/Llama-3.2-1B-Instruct
 library_name: transformers
+pipeline_tag: text-generation
 tags:
+  - llama
+  - mergekit
+  - merge
+  - slerp
+  - text-generation
+  - code
+  - instruct
 datasets:
+  - OpenCoder-LLM/opc-sft-stage1
+  - OpenCoder-LLM/opc-sft-stage2
+  - microsoft/orca-agentinstruct-1M-v1
+  - microsoft/orca-math-word-problems-200k
+  - NousResearch/hermes-function-calling-v1
+  - AI-MO/NuminaMath-CoT
+  - AI-MO/NuminaMath-TIR
+  - allenai/tulu-3-sft-mixture
+  - HuggingFaceTB/smoltalk
+  - m-a-p/CodeFeedback-Filtered-Instruction
+  - m-a-p/Code-Feedback
+  - teknium/OpenHermes-2.5
 ---
+# Llama-3.2-HermesDolphin-Coder-1B
+Llama-3.2-HermesDolphin-Coder-1B is a compact merged language model designed for general instruction following, coding assistance, and lightweight conversational use. It combines Hermes-style instruction tuning and Dolphin-style helpfulness into a small Llama 3.2 class model intended for experimentation, local workflows, and developer-oriented prompting.
+This repository appears to be a **merge model** created with **mergekit** using the **SLERP** merge method.
+## Model Summary
+- **Model type:** Causal language model
+- **Architecture:** LlamaForCausalLM
+- **Primary use:** Text generation, instruction following, code-oriented prompting
+- **Library:** Transformers
+- **Merge method:** SLERP
+- **Format:** Safetensors
+## Base Models
+This merged model is based on:
+- `artificialguybr/LLAMA-3.2-1B-OpenHermes2.5`
+- `dphn/Dolphin3.0-Llama3.2-1B`
+- `meta-llama/Llama-3.2-1B-Instruct`
+## Merge Details
+According to the repository metadata/configuration, the merge was produced with `mergekit` using a SLERP setup with a midpoint interpolation parameter.
+### Merge configuration
 ```yaml
 merge_method: slerp
+base_model: artificialguybr/LLAMA-3.2-1B-OpenHermes2.5
 models:
+  - model: dphn/Dolphin3.0-Llama3.2-1B
+    parameters:
+      weight: 1.0
 dtype: float32
 parameters:
+  t: 0.5