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
```

zamba2-7b-instruct-hxq / dense_7b_eval.json

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Upload dense_7b_eval.json with huggingface_hub

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	{
	"model": "/home/user/models/zamba2-7b-instruct",
	"tasks": [
	"hellaswag",
	"arc_challenge",
	"arc_easy"
	],
	"batch_size": 4,
	"results": {
	"arc_challenge": {
	"acc,none": 0.568259385665529,
	"acc_stderr,none": 0.014474591427196202,
	"acc_norm,none": 0.5938566552901023,
	"acc_norm_stderr,none": 0.014351656690097856
	},
	"arc_easy": {
	"acc,none": 0.8152356902356902,
	"acc_stderr,none": 0.007963772171570793,
	"acc_norm,none": 0.8320707070707071,
	"acc_norm_stderr,none": 0.007670285673828055
	},
	"hellaswag": {
	"acc,none": 0.6238797052380004,
	"acc_stderr,none": 0.004834207964061318,
	"acc_norm,none": 0.8079067914758016,
	"acc_norm_stderr,none": 0.003931408309245506
	}
	},
	"vram_peak_mb": 23865.0,
	"cost": {
	"wall_time_s": 3256.124,
	"cpu_time_s": 3255.653,
	"peak_memory_mb": 5716.7,
	"python_version": "3.10.12",
	"hostname": "8552b21e-f647-40e8-ab36-30dc6fb62665",
	"timestamp_start": "2026-04-02T16:43:37",
	"timestamp_end": "2026-04-02T17:37:53",
	"lm_eval_version": "0.4.11",
	"helix_substrate_version": "0.3.0"
	}
	}