Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tda45/TdAI with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tda45/TdAI",
	filename="llama.cpp/models/ggml-vocab-aquila.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tda45/TdAI with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tda45/TdAI
# Run inference directly in the terminal:
llama cli -hf tda45/TdAI

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tda45/TdAI
# Run inference directly in the terminal:
llama cli -hf tda45/TdAI

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf tda45/TdAI
# Run inference directly in the terminal:
./llama-cli -hf tda45/TdAI

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf tda45/TdAI
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tda45/TdAI

Use Docker

docker model run hf.co/tda45/TdAI

LM Studio
Jan
Ollama
How to use tda45/TdAI with Ollama:
```
ollama run hf.co/tda45/TdAI
```

Unsloth Studio

How to use tda45/TdAI with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tda45/TdAI to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tda45/TdAI to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tda45/TdAI to start chatting

Atomic Chat new
Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
```
docker model run hf.co/tda45/TdAI
```

Lemonade

How to use tda45/TdAI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tda45/TdAI

Run and chat with the model

lemonade run user.TdAI-{{QUANT_TAG}}

List all available models

lemonade list

TdAI / llama.cpp /ggml /src /ggml-hexagon /htp /cpy-ops.c

tda45

Upload folder using huggingface_hub (part 4)

20a07ae verified 11 days ago

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	#pragma clang diagnostic ignored "-Wunused-variable"
	#pragma clang diagnostic ignored "-Wunused-function"
	#pragma clang diagnostic ignored "-Wunused-but-set-variable"

	#include <HAP_farf.h>
	#include <HAP_perf.h>

	#include <math.h>
	#include <string.h>

	#define GGML_COMMON_DECL_C
	#include "ggml-common.h"
	#include "htp-ctx.h"
	#include "htp-ops.h"
	#include "htp-ops.h"
	#include "hvx-utils.h"

	struct htp_copy_context {
	struct htp_ops_context * octx;

	uint32_t src0_type_size;
	uint32_t src0_block_size;

	uint32_t dst_type_size;
	uint32_t dst_block_size;

	uint32_t src0_blocks_per_row;
	uint32_t dst_blocks_per_row;

	uint32_t src0_nrows_per_thread;
	};

	#define cpy_preamble \
	const struct htp_tensor *src0 = octx->src[0]; \
	const struct htp_tensor *dst = octx->dst; \
	\
	const uint32_t ne00 = src0->ne[0]; \
	const uint32_t ne01 = src0->ne[1]; \
	const uint32_t ne02 = src0->ne[2]; \
	const uint32_t ne03 = src0->ne[3]; \
	\
	const uint32_t nb00 = src0->nb[0]; \
	const uint32_t nb01 = src0->nb[1]; \
	const uint32_t nb02 = src0->nb[2]; \
	const uint32_t nb03 = src0->nb[3]; \
	\
	const uint32_t ne0 = dst->ne[0]; \
	const uint32_t ne1 = dst->ne[1]; \
	const uint32_t ne2 = dst->ne[2]; \
	const uint32_t ne3 = dst->ne[3]; \
	\
	const uint32_t nb0 = dst->nb[0]; \
	const uint32_t nb1 = dst->nb[1]; \
	const uint32_t nb2 = dst->nb[2]; \
	const uint32_t nb3 = dst->nb[3]; \
	\
	const uint32_t nr = ne01;

	#define DEFINE_CPY_SAMESHAPE(NAME, ELEM_TYPE, ELEM_SIZE) \
	static void cpy_thread_##NAME##_sameshape(unsigned int nth, unsigned int ith, void * data) { \
	struct htp_copy_context * ct = (struct htp_copy_context *) data; \
	struct htp_ops_context * octx = ct->octx; \
	cpy_preamble; \
	const uint32_t dr = ct->src0_nrows_per_thread; \
	const uint32_t ir0 = dr * ith; \
	const uint32_t ir1 = (ir0 + dr) < nr ? (ir0 + dr) : nr; \
	if (ir0 >= nr) return; \
	for (uint32_t i03 = 0; i03 < ne03; i03++) { \
	for (uint32_t i02 = 0; i02 < ne02; i02++) { \
	_Pragma("unroll(4)") \
	for (uint32_t i01 = ir0; i01 < ir1; i01++) { \
	uint8_t* dst_ptr = (uint8_t) dst->data + i01nb1 + i02nb2 + i03nb3; \
	uint8_t* src0_ptr = (uint8_t) src0->data + i01nb01 + i02nb02 + i03nb03; \
	hex_l2fetch(src0_ptr, ne00 * ELEM_SIZE, nb01, 2); \
	hvx_copy_uu(dst_ptr, src0_ptr, ne00, ELEM_SIZE); \
	} \
	} \
	} \
	}

	DEFINE_CPY_SAMESHAPE(f32, float, 4)
	DEFINE_CPY_SAMESHAPE(f16, __fp16, 2)

	#define DEFINE_CPY_RESHAPE(NAME, ELEM_TYPE, ELEM_SIZE) \
	static void cpy_thread_##NAME##_reshape(unsigned int nth, unsigned int ith, void * data) { \
	struct htp_copy_context * ct = (struct htp_copy_context *) data; \
	struct htp_ops_context * octx = ct->octx; \
	cpy_preamble; \
	const uint32_t dr = ct->src0_nrows_per_thread; \
	const uint32_t ir0 = dr * ith; \
	const uint32_t ir1 = (ir0 + dr) < nr ? (ir0 + dr) : nr; \
	if (ir0 >= nr) return; \
	const bool src0_contig = (nb00 == ELEM_SIZE) && \
	(nb01 == ne00 * nb00) && \
	(nb02 == ne01 * nb01) && \
	(nb03 == ne02 * nb02); \
	const bool dst_contig = (nb0 == ELEM_SIZE) && \
	(nb1 == ne0 * nb0) && \
	(nb2 == ne1 * nb1) && \
	(nb3 == ne2 * nb2); \
	if (src0_contig && dst_contig) { \
	for (int64_t i03 = 0; i03 < ne03; i03++) { \
	for (int64_t i02 = 0; i02 < ne02; i02++) { \
	uint8_t * src_ptr = (uint8_t ) src0->data + i03nb03 + i02nb02 + ir0nb01; \
	uint32_t flat = ((i03ne02 + i02)ne01 + ir0) * ne00; \
	uint8_t * dst_ptr = (uint8_t ) dst->data + flat ELEM_SIZE; \
	hvx_copy_uu(dst_ptr, src_ptr, (ir1 - ir0) * ne00, ELEM_SIZE); \
	} \
	} \
	return; \
	} \
	const bool reshape_flat_fast = (ne03 == 1 && ne2 == 1 && ne3 == 1) && \
	(ne0 == ne00 * ne01) && (ne1 == ne02) && \
	(nb00 == ELEM_SIZE) && (nb0 == ELEM_SIZE); \
	if (reshape_flat_fast) { \
	for (uint32_t i02 = 0; i02 < ne02; i02++) { \
	for (uint32_t i01 = ir0; i01 < ir1; i01++) { \
	uint8_t * src0_ptr = (uint8_t ) src0->data + i01 nb01 + i02 * nb02; \
	uint8_t * dst_ptr = (uint8_t ) dst->data + i01 ne00 * ELEM_SIZE + i02 * nb1; \
	hvx_copy_uu(dst_ptr, src0_ptr, ne00, ELEM_SIZE); \
	} \
	} \
	return; \
	} \
	int64_t k10 = 0; \
	int64_t i11 = 0; \
	int64_t i12 = 0; \
	int64_t i13 = 0; \
	const int64_t nk00 = ct->src0_blocks_per_row; \
	const int64_t nk0 = ct->dst_blocks_per_row; \
	for (int64_t i03 = 0; i03 < ne03; i03++) { \
	for (int64_t i02 = 0; i02 < ne02; i02++) { \
	k10 += nk00 * ir0; \
	while (k10 >= nk0) { \
	k10 -= nk0; \
	if (++i11 == ne1) { \
	i11 = 0; \
	if (++i12 == ne2) { \
	i12 = 0; \
	if (++i13 == ne3) { \
	i13 = 0; \
	} \
	} \
	} \
	} \
	for (int64_t i01 = ir0; i01 < ir1; i01++) { \
	for (int64_t k00 = 0; k00 < nk00; k00++) { \
	const char * src0_ptr = ((char ) src0->data + k00nb00 + i01nb01 + i02nb02 + i03*nb03); \
	char * dst_ptr = ((char ) dst->data + k10nb0 + i11nb1 + i12nb2 + i13*nb3); \
	memcpy(dst_ptr, src0_ptr, ELEM_SIZE); \
	if (++k10 == nk0) { \
	k10 = 0; \
	if (++i11 == ne1) { \
	i11 = 0; \
	if (++i12 == ne2) { \
	i12 = 0; \
	if (++i13 == ne3) { \
	i13 = 0; \
	} \
	} \
	} \
	} \
	} \
	} \
	k10 += nk00 * (ne01 - ir1); \
	while (k10 >= nk0) { \
	k10 -= nk0; \
	if (++i11 == ne1) { \
	i11 = 0; \
	if (++i12 == ne2) { \
	i12 = 0; \
	if (++i13 == ne3) { \
	i13 = 0; \
	} \
	} \
	} \
	} \
	} \
	} \
	}

	DEFINE_CPY_RESHAPE(f32, float, 4)
	DEFINE_CPY_RESHAPE(f16, __fp16, 2)

	static void cpy_thread_f16_f32_sameshape(unsigned int nth, unsigned int ith, void * data) {
	struct htp_copy_context * ct = (struct htp_copy_context *) data;
	struct htp_ops_context * octx = ct->octx;
	cpy_preamble;

	// parallelize by src0 rows
	const uint32_t dr = ct->src0_nrows_per_thread;
	const uint32_t ir0 = dr * ith;
	const uint32_t ir1 = (ir0 + dr) < nr ? (ir0 + dr) : nr;
	if (ir0 >= nr) return;

	// copy by rows
	for (uint32_t i03 = 0; i03 < ne03; i03++) {
	for (uint32_t i02 = 0; i02 < ne02; i02++) {
	#pragma unroll(2)
	for (uint32_t i01 = ir0; i01 < ir1; i01++) {
	uint8_t* dst_ptr = (uint8_t) dst->data + i01nb1 + i02nb2 + i03nb3;
	uint8_t* src0_ptr = (uint8_t) src0->data + i01nb01 + i02nb02 + i03nb03;
	hex_l2fetch(src0_ptr, ne00 * sizeof(float), nb01, 2);
	hvx_copy_f16_f32_uu(dst_ptr, src0_ptr, ne00);
	}
	}
	}
	}

	static void cpy_thread_f32_f16_sameshape(unsigned int nth, unsigned int ith, void * data) {
	struct htp_copy_context * ct = (struct htp_copy_context *) data;
	struct htp_ops_context * octx = ct->octx;
	cpy_preamble;

	// parallelize by src0 rows
	const uint32_t dr = ct->src0_nrows_per_thread;
	const uint32_t ir0 = dr * ith;
	const uint32_t ir1 = (ir0 + dr) < nr ? (ir0 + dr) : nr;
	if (ir0 >= nr) return;

	// copy by rows
	for (uint32_t i03 = 0; i03 < ne03; i03++) {
	for (uint32_t i02 = 0; i02 < ne02; i02++) {
	#pragma unroll(2)
	for (uint32_t i01 = ir0; i01 < ir1; i01++) {
	uint8_t* dst_ptr = (uint8_t) dst->data + i01nb1 + i02nb2 + i03nb3;
	uint8_t* src0_ptr = (uint8_t) src0->data + i01nb01 + i02nb02 + i03nb03;
	hex_l2fetch(src0_ptr, ne00 * sizeof(__fp16), nb01, 2);
	hvx_copy_f32_f16_uu(dst_ptr, src0_ptr, ne00);
	}
	}
	}
	}

	int op_cpy(struct htp_ops_context * octx) {
	cpy_preamble;

	const uint32_t n_threads = MIN(nr, octx->n_threads);

	struct htp_copy_context ct;
	ct.octx = octx;

	switch (src0->type) {
	case HTP_TYPE_F32: ct.src0_type_size = 4; ct.src0_block_size = 1; ct.src0_blocks_per_row = ne00 / 1; break;
	case HTP_TYPE_F16: ct.src0_type_size = 2; ct.src0_block_size = 1; ct.src0_blocks_per_row = ne00 / 1; break;
	default:
	return HTP_STATUS_NO_SUPPORT;
	}

	switch (dst->type) {
	case HTP_TYPE_F32: ct.dst_type_size = 4; ct.dst_block_size = 1; ct.dst_blocks_per_row = ne0 / 1; break;
	case HTP_TYPE_F16: ct.dst_type_size = 2; ct.dst_block_size = 1; ct.dst_blocks_per_row = ne0 / 1; break;
	default:
	return HTP_STATUS_NO_SUPPORT;
	}

	if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
	return HTP_STATUS_OK;
	}

	const bool sametype = (src0->type == dst->type);
	const bool transposed = (nb00 > nb01) \|\| (nb0 > nb1);
	const bool sameshape = !transposed && (ne00 == ne0 && ne01 == ne1 && ne02 == ne2 && ne03 == ne3);

	ct.src0_nrows_per_thread = (nr + n_threads - 1) / n_threads;

	worker_callback_t copy_fun;

	if (sametype && sameshape) {
	if (src0->type == HTP_TYPE_F32) {
	copy_fun = cpy_thread_f32_sameshape;
	} else {
	copy_fun = cpy_thread_f16_sameshape;
	}
	} else if (sameshape) {
	/**/ if (dst->type == HTP_TYPE_F16 && src0->type == HTP_TYPE_F32)
	copy_fun = cpy_thread_f16_f32_sameshape;
	else if (dst->type == HTP_TYPE_F32 && src0->type == HTP_TYPE_F16)
	copy_fun = cpy_thread_f32_f16_sameshape;
	else
	return HTP_STATUS_NO_SUPPORT;
	} else if (sametype) {
	if (src0->type == HTP_TYPE_F32) {
	copy_fun = cpy_thread_f32_reshape;
	} else {
	copy_fun = cpy_thread_f16_reshape;
	}
	} else {
	return HTP_STATUS_NO_SUPPORT;
	}

	worker_pool_run_func(octx->ctx->worker_pool, copy_fun, &ct, n_threads);

	return HTP_STATUS_OK;
	}