DeepSeek-R1-Distill-Qwen-32B-FP8-Dynamic API & Inference Endpoint

Deployment

Use with SGLang

bash
python -m sglang.launch_server --model-path JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-32B-FP8-Dynamic \
--port 30000 --host 0.0.0.0

Creation

This model was created with llm-compressor by running the code snippet below.

python
from transformers import AutoModelForCausalLM, AutoTokenizer

from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot

MODEL_ID = "google/gemma-2-27b-it"

# 1) Load model.
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, device_map="auto", torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# 2) Configure the quantization algorithm and scheme.
# In this case, we:
#   * quantize the weights to fp8 with per channel via ptq
#   * quantize the activations to fp8 with dynamic per token
recipe = QuantizationModifier(
    targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
)

# 3) Apply quantization and save in compressed-tensors format.
OUTPUT_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
oneshot(
    model=model,
    recipe=recipe,
    tokenizer=tokenizer,
    output_dir=OUTPUT_DIR,
)

# Confirm generations of the quantized model look sane.
print("========== SAMPLE GENERATION ==============")
input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))
print("==========================================")

Evaluation

TBA

Play Retail Mage

image/png

Retail Mage (Steam) is an immersive sim that uses online LLM inference in almost all features in the gameplay!

Reviews

“A true to life experience detailing how customer service really works.” 10/10 – kpolupo

“I enjoyed how many things were flammable in the store.” 5/5 – mr_srsbsns

“I've only known that talking little crow plushie in MageMart for a day and a half but if anything happened to him I would petrify everyone in this store and then myself.” 7/7 – neondenki

Deployment

Use with SGLang

bash
python -m sglang.launch_server --model-path JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-32B-FP8-Dynamic \
--port 30000 --host 0.0.0.0

Creation

This model was created with llm-compressor by running the code snippet below.

python
from transformers import AutoModelForCausalLM, AutoTokenizer

from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot

MODEL_ID = "google/gemma-2-27b-it"

# 1) Load model.
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, device_map="auto", torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# 2) Configure the quantization algorithm and scheme.
# In this case, we:
#   * quantize the weights to fp8 with per channel via ptq
#   * quantize the activations to fp8 with dynamic per token
recipe = QuantizationModifier(
    targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
)

# 3) Apply quantization and save in compressed-tensors format.
OUTPUT_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
oneshot(
    model=model,
    recipe=recipe,
    tokenizer=tokenizer,
    output_dir=OUTPUT_DIR,
)

# Confirm generations of the quantized model look sane.
print("========== SAMPLE GENERATION ==============")
input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))
print("==========================================")

Evaluation

TBA

Play Retail Mage

image/png

Retail Mage (Steam) is an immersive sim that uses online LLM inference in almost all features in the gameplay!

Reviews

“A true to life experience detailing how customer service really works.” 10/10 – kpolupo

“I enjoyed how many things were flammable in the store.” 5/5 – mr_srsbsns

“I've only known that talking little crow plushie in MageMart for a day and a half but if anything happened to him I would petrify everyone in this store and then myself.” 7/7 – neondenki

DeepSeek-R1-Distill-Qwen-32B-FP8-Dynamic

README

Deployment

Use with SGLang

Creation

Evaluation

Play Retail Mage

Explore FriendliAI today

README

Deployment

Use with SGLang

Creation

Evaluation

Play Retail Mage