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C10X/
Qwen3-6.4m-falcon

Name: Qwen3-6.4m-falcon
Availability: InStock

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Qwen3 16M Model with Falcon-H1-0.5B-Instruct Tokenizer

Model Description

This is a 6.4M parameter Qwen3 model architecture combined with the Falcon-H1-0.5B-Instruct tokenizer (32K vocabulary).

Architecture: Qwen3 (Grouped Query Attention, RMS Normalization, Q/K Normalization, RoPE)
Tokenizer: Falcon-H1-0.5B-Instruct (32K vocab)
Parameters: 6,393,440
Precision: BF16
Format: SafeTensors
Vocabulary Size: 32768
Use Case: Desktop applications, balanced performance (~10M params)

Configuration

vocab_size: 32768
hidden_size: 96
num_attention_heads: 8
num_key_value_heads: 4
num_hidden_layers: 8
intermediate_size: 384
head_dim: 128
max_position_embeddings: 8192

Special Tokens

BOS: <|begin_of_text|> (id: 17)
EOS: <|end_of_text|> (id: 11)
PAD: <|pad|> (id: 0)

Usage

python

from transformers import Qwen3ForCausalLM, AutoTokenizer

model = Qwen3ForCausalLM.from_pretrained("./workspace/16m-falcon-tokenizer")
tokenizer = AutoTokenizer.from_pretrained("./workspace/16m-falcon-tokenizer")

# Generate text
inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Batch processing (start small)
texts = ["Hello", "How are you", "Good morning"]
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=20)

Important Notes

Model uses Qwen3 architecture with Falcon tokenizer (32K vocabulary)
All token IDs must be < 32768 to avoid CUDA errors
Start with small batch sizes (1-4) and gradually increase
Use proper padding to prevent dimension mismatches
Model initialized with random weights - requires fine-tuning
Compatible with Qwen3 APIs but uses Falcon vocabulary

C10X/Qwen3-6.4m-falcon