Falcon-E-3B-Instruct-GGUF

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Edge AI:
Mobile
Laptop
Server
7GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
3GB+ RAM

Training Data Analysis

🔴 Low Quality (2.5/10)

Researched training datasets used by Falcon-E-3B-Instruct-GGUF with quality assessment

Specialized For

general
science

Training Datasets (1)

common crawl
🔴 2.5/10
general
science
Key Strengths
  • •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Inferencetext
git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
huggingface-cli download tiiuae/Falcon-E-3B-Instruct-GGUF ggml-model-i2_s.gguf --local-dir models/Falcon-E-3B-Instruct/
python run_inference.py -m models/Falcon-E-3B-Instruct/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)
Fine-tuningdifftransformers
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

model_id = "tiiuae/Falcon-E-1B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)

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