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

a6390f5

verified ·

1 Parent(s): 9d4d8de

add: RTX 4090 benchmark receipt (Dense, bnb-8bit, bnb-4bit, HXQ 2D VQ)

Browse files

Files changed (1) hide show

zamba2_7b_comparison.json +71 -0

zamba2_7b_comparison.json ADDED Viewed

	@@ -0,0 +1,71 @@

+{
+  "experiment": "WO-BNB-BASELINE-01",
+  "model": "Zamba2-7B-Instruct",
+  "eval_params": {
+    "dataset": "wikitext/wikitext-2-raw-v1/test",
+    "max_length": 2048,
+    "stride": 512,
+    "speed_iters": 100,
+    "speed_seq_len": 512
+  },
+  "configs": {
+    "dense_bf16": {
+      "name": "Dense BF16",
+      "ppl": 3.7216,
+      "n_tokens": 334660,
+      "n_chunks": 651,
+      "vram_load_mb": 14032.0,
+      "vram_peak_eval_mb": 16173.8,
+      "vram_peak_speed_mb": 14396.5,
+      "tokens_per_sec": 4385.2,
+      "speed_test_elapsed_s": 11.68,
+      "config_elapsed_s": 297.3
+    },
+    "bnb_8bit": {
+      "name": "bnb 8-bit (LLM.int8)",
+      "ppl": 3.7335,
+      "n_tokens": 334660,
+      "n_chunks": 651,
+      "vram_load_mb": 7831.3,
+      "vram_peak_eval_mb": 10011.6,
+      "vram_peak_speed_mb": 8188.0,
+      "tokens_per_sec": 2165.3,
+      "speed_test_elapsed_s": 23.65,
+      "config_elapsed_s": 334.3
+    },
+    "bnb_4bit_nf4": {
+      "name": "bnb 4-bit NF4",
+      "ppl": 3.8916,
+      "n_tokens": 334660,
+      "n_chunks": 651,
+      "vram_load_mb": 5106.6,
+      "vram_peak_eval_mb": 7240.4,
+      "vram_peak_speed_mb": 5590.6,
+      "tokens_per_sec": 3468.4,
+      "speed_test_elapsed_s": 14.76,
+      "config_elapsed_s": 332.6
+    },
+    "hxq_2dvq_k4096": {
+      "name": "HXQ 2D VQ k=4096",
+      "ppl": 3.8454,
+      "n_tokens": 334660,
+      "n_chunks": 651,
+      "vram_load_mb": 14040.1,
+      "vram_peak_eval_mb": 16173.8,
+      "vram_peak_speed_mb": 14396.5,
+      "tokens_per_sec": 4384.5,
+      "speed_test_elapsed_s": 11.68,
+      "config_elapsed_s": 343.0
+    }
+  },
+  "cost": {
+    "wall_time_s": 1311.822,
+    "cpu_time_s": 1440.214,
+    "peak_memory_mb": 16490.8,
+    "python_version": "3.10.12",
+    "hostname": "74efa4ce-5ef6-427c-b27b-336ce89875b5",
+    "timestamp_start": "2026-04-01T21:29:26",
+    "timestamp_end": "2026-04-01T21:51:18",
+    "gpu": "NVIDIA GeForce RTX 4090"
+  }
+}