Instructions to use EchoLabs33/zamba2-7b-instruct-hxq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EchoLabs33/zamba2-7b-instruct-hxq with Transformers:

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

pipe = pipeline("text-generation", model="EchoLabs33/zamba2-7b-instruct-hxq")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EchoLabs33/zamba2-7b-instruct-hxq")
model = AutoModelForCausalLM.from_pretrained("EchoLabs33/zamba2-7b-instruct-hxq", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EchoLabs33/zamba2-7b-instruct-hxq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EchoLabs33/zamba2-7b-instruct-hxq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EchoLabs33/zamba2-7b-instruct-hxq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EchoLabs33/zamba2-7b-instruct-hxq

SGLang

How to use EchoLabs33/zamba2-7b-instruct-hxq 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 "EchoLabs33/zamba2-7b-instruct-hxq" \
    --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": "EchoLabs33/zamba2-7b-instruct-hxq",
		"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 "EchoLabs33/zamba2-7b-instruct-hxq" \
        --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": "EchoLabs33/zamba2-7b-instruct-hxq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EchoLabs33/zamba2-7b-instruct-hxq with Docker Model Runner:
```
docker model run hf.co/EchoLabs33/zamba2-7b-instruct-hxq
```

voidstream commited on Apr 2

Commit

825409e

verified ·

1 Parent(s): e0a0003

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +39 -0

README.md CHANGED Viewed

@@ -25,6 +25,33 @@ model-index:
           - name: Perplexity
             type: perplexity
             value: 3.8454
 ---
 # Zamba2-7B-Instruct-HXQ
@@ -51,6 +78,18 @@ All numbers from a single session, same GPU, same WikiText-2 test set (50 chunks
 - **Better quality than bnb 4-bit** (5.02 vs 5.07 PPL) at comparable VRAM
 - **No calibration data required** -- unlike GPTQ, AWQ, or bnb, HXQ compresses from weights alone
 ## Install and Run
 ```bash

           - name: Perplexity
             type: perplexity
             value: 3.8454
+      - task:
+          type: text-generation
+        dataset:
+          name: HellaSwag
+          type: hellaswag
+        metrics:
+          - name: acc_norm
+            type: acc_norm
+            value: 0.8106
+      - task:
+          type: text-generation
+        dataset:
+          name: ARC-Challenge
+          type: ai2_arc
+        metrics:
+          - name: acc_norm
+            type: acc_norm
+            value: 0.5811
+      - task:
+          type: text-generation
+        dataset:
+          name: ARC-Easy
+          type: ai2_arc
+        metrics:
+          - name: acc_norm
+            type: acc_norm
+            value: 0.8190
 ---
 # Zamba2-7B-Instruct-HXQ
 - **Better quality than bnb 4-bit** (5.02 vs 5.07 PPL) at comparable VRAM
 - **No calibration data required** -- unlike GPTQ, AWQ, or bnb, HXQ compresses from weights alone
+## Downstream Task Evaluation (lm-eval-harness v0.4.11)
+Same GPU (RTX 3090), same harness, same settings. All metrics are `acc_norm`.
+| Task | Dense BF16 | HXQ 2D VQ | Delta |
+|------|-----------|-----------|-------|
+| **HellaSwag** | 80.79% | 81.06% | +0.27% |
+| **ARC-Challenge** | 59.39% | 58.11% | -1.28% |
+| **ARC-Easy** | 83.21% | 81.90% | -1.31% |
+Compression preserves task performance within noise. HellaSwag (commonsense reasoning) is slightly *better* under compression. ARC drops are within 1.3%.
 ## Install and Run
 ```bash